code stringlengths 3 6.57k |
|---|
res.keys() |
expected.keys() |
assert_allclose(res['translations'], expected['translations'], rtol=1e-4, atol=1e-4) |
assert_allclose(res['center'], expected['center'], rtol=1e-4, atol=1e-4) |
assert_allclose(res['scale'], expected['scale'], rtol=1e-4, atol=1e-4) |
assert_allclose(res['angle'], expected['angle'], rtol=1e-4, atol=1e-4) |
assert_allclose(res['sx'], expected['sx'], rtol=1e-4, atol=1e-4) |
assert_allclose(res['sy'], expected['sy'], rtol=1e-4, atol=1e-4) |
TestRandomRotationGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('batch_size', [0, 1, 8]) |
pytest.mark.parametrize('degrees', [torch.tensor([0, 30]) |
pytest.mark.parametrize('same_on_batch', [True, False]) |
test_valid_param_combinations(self, batch_size, degrees, same_on_batch, device, dtype) |
degrees.to(device=device, dtype=dtype) |
pytest.mark.parametrize('degrees', [(torch.tensor(10) |
torch.tensor([10]) |
torch.tensor([10, 20, 30]) |
test_invalid_param_combinations(self, degrees, device, dtype) |
pytest.raises(Exception) |
random_rotation_generator(batch_size=batch_size, degrees=degrees.to(device=device, dtype=dtype) |
test_random_gen(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([10, 20]) |
degrees.to(device=device, dtype=dtype) |
dict(degrees=torch.tensor([18.8227, 19.1500], device=device, dtype=dtype) |
res.keys() |
expected.keys() |
assert_allclose(res['degrees'], expected['degrees']) |
test_same_on_batch(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([10, 20]) |
degrees.to(device=device, dtype=dtype) |
dict(degrees=torch.tensor([18.8227, 18.8227], device=device, dtype=dtype) |
res.keys() |
expected.keys() |
assert_allclose(res['degrees'], expected['degrees']) |
TestRandomCropGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('batch_size', [0, 2]) |
pytest.mark.parametrize('input_size', [(200, 200) |
pytest.mark.parametrize('size', [(100, 100) |
torch.tensor([50, 50]) |
pytest.mark.parametrize('resize_to', [None, (100, 100) |
pytest.mark.parametrize('same_on_batch', [True, False]) |
test_valid_param_combinations(self, batch_size, input_size, size, resize_to, same_on_batch, device, dtype) |
isinstance(size, torch.Tensor) |
size.repeat(batch_size, 1) |
to(device=device, dtype=dtype) |
torch.tensor([50, 50]) |
test_invalid_param_combinations(self, input_size, size, resize_to, device, dtype) |
pytest.raises(Exception) |
size.to(device=device, dtype=dtype) |
isinstance(size, torch.Tensor) |
test_random_gen(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([10, 20], device=device, dtype=dtype) |
torch.tensor([[50, 60], [70, 80]], device=device, dtype=dtype) |
torch.tensor([[100, 100], [100, 100]], device=device, dtype=torch.long) |
res.keys() |
expected.keys() |
assert_allclose(res['src'], expected['src']) |
assert_allclose(res['dst'], expected['dst']) |
test_same_on_batch(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([10, 20], device=device, dtype=dtype) |
torch.tensor([[50, 60], [70, 80]], device=device, dtype=dtype) |
torch.tensor([[100, 100], [100, 100]], device=device, dtype=torch.long) |
res.keys() |
expected.keys() |
assert_allclose(res['src'], expected['src']) |
assert_allclose(res['dst'], expected['dst']) |
TestRandomCropSizeGen(RandomGeneratorBaseTests) |
pytest.mark.parametrize('batch_size', [0, 1, 8]) |
pytest.mark.parametrize('size', [(200, 200) |
pytest.mark.parametrize('scale', [torch.tensor([.7, 1.3]) |
pytest.mark.parametrize('ratio', [torch.tensor([.9, 1.1]) |
pytest.mark.parametrize('same_on_batch', [True, False]) |
test_valid_param_combinations(self, batch_size, size, scale, ratio, same_on_batch, device, dtype) |
scale.to(device=device, dtype=dtype) |
ratio.to(device=device, dtype=dtype) |
torch.tensor([.7, 1.3]) |
torch.tensor([.9, 1.1]) |
torch.tensor([.7, 1.3]) |
torch.tensor([.9, 1.1]) |
torch.tensor([.7]) |
torch.tensor([.9, 1.1]) |
torch.tensor([.7, 1.3, 1.5]) |
torch.tensor([.9, 1.1]) |
torch.tensor([.7, 1.3]) |
torch.tensor([.9]) |
torch.tensor([.7, 1.3]) |
torch.tensor([.9, 1.1, 1.3]) |
test_invalid_param_combinations(self, size, scale, ratio, device, dtype) |
pytest.raises(Exception) |
scale.to(device=device, dtype=dtype) |
ratio.to(device=device, dtype=dtype) |
test_random_gen(self, device, dtype) |
torch.manual_seed(42) |
torch.tensor([0.7, 1.3], device=device, dtype=dtype) |
torch.tensor([0.9, 1.1], device=device, dtype=dtype) |
res.keys() |
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